Ethical AI Use Guidelines
These guidelines help organizations use AI responsibly, protecting stakeholders while capturing value. Read the principles, then use the readiness audit and decision log at the end to apply them before you deploy anything.
Core Principles
1. Transparency
People should know when AI is involved in decisions that affect them. Disclose AI use in customer-facing applications. Make AI decision criteria explainable. Don't pretend AI outputs are human-generated. Document AI use in internal processes.
2. Human Accountability
AI is a tool. Humans are responsible for its use. Every AI system needs a human owner. Humans review AI decisions that matter. Accountability can't be delegated to machines. "The AI did it" is not an excuse.
3. Fairness
AI should not perpetuate or amplify bias. Audit AI outputs for biased patterns. Test with diverse inputs and scenarios. Monitor for disparate impact. Be especially careful with decisions affecting people's lives.
4. Privacy
AI must respect data privacy and consent. Only use data you have permission to use. Minimize data collection to what's necessary. Protect data used in AI systems. Don't use AI to infer sensitive information.
5. Beneficence
AI should create more value than harm. Consider who benefits and who might be harmed. Weigh benefits against risks honestly. Don't deploy AI just because you can. Prioritize stakeholder wellbeing.
Guidelines by Use Case
Customer-Facing AI
Chatbots and assistants: disclose that customers are interacting with AI, provide easy escalation to humans, don't collect unnecessary personal data, monitor for inappropriate responses. Recommendations: be transparent about what drives them, let users control preferences, don't manipulate toward harmful choices. Automated decisions: explain how decisions are made, provide meaningful appeals, audit for bias, keep humans in the loop for consequential calls.
Internal Operations AI
Process automation: document what's automated and why, keep human oversight of critical processes, have rollback plans. Data analysis: don't draw conclusions beyond what the data supports, be transparent about confidence, remember correlation isn't causation. Content generation: review before publishing, disclose when content is AI-assisted, verify facts, keep your authentic voice.
Red Lines
Never Use AI To:
- Deceive — fake identities, deepfakes, deliberately misleading content
- Manipulate — exploiting psychological vulnerabilities or dark patterns
- Discriminate — decisions based on protected characteristics
- Surveil — monitoring people without knowledge or consent
- Replace critical judgment — decisions requiring human ethics
- Harm — any use intended to damage people or organizations
High-Risk Uses Requiring Extra Scrutiny:
Employment decisions (hiring, firing, promotions), financial decisions (lending, insurance, pricing), healthcare decisions (diagnosis, treatment, coverage), legal decisions (bail, sentencing, parole), and access decisions (housing, education, services).
Put It to Work, Part 1: The Five-Principle Readiness Audit
Before deploying any AI, score it against each principle (1 = not addressed, 5 = fully addressed).
AI use being evaluated: _________________________________
| Principle | Score (1–5) | What's still missing |
|---|---|---|
| Transparency — people know AI is involved | ||
| Accountability — a named human owns it | ||
| Fairness — tested for bias | ||
| Privacy — data is consented and minimized | ||
| Beneficence — benefit clearly outweighs harm |
Total / 25: _______ — Any principle scored 1–2 is a blocker. Resolve it before deploying.
Put It to Work, Part 2: Red-Line & Risk Check
| Check | Answer |
|---|---|
| Does this touch any red-line use (deceive, manipulate, discriminate, surveil, replace judgment, harm)? | ☐ Yes ☐ No |
| Is this a high-risk domain (employment, finance, health, legal, access)? | ☐ Yes ☐ No |
| If yes to either — what extra safeguard is in place? | |
| Who can a person appeal to for human review? |
If you checked "Yes" to a red-line use, stop. Do not deploy.
Put It to Work, Part 3: Deployment Decision Log
| Phase | Question | Done? |
|---|---|---|
| Planning | Purpose defined, impact assessed, risks mitigated | [ ] |
| Development | Data ethically sourced, bias tested, privacy built in | [ ] |
| Deployment | Users informed, feedback + escalation live, rollback ready | [ ] |
| Operations | Audits scheduled, drift monitored, incidents reviewed | [ ] |
When In Doubt, Ask:
- Would I be comfortable if this AI use were public?
- Would I want this AI making decisions about me?
- Who could be harmed, and is that acceptable?
- Are we being honest about what this AI does?
- Do we have meaningful human oversight?
My decision:
If any answer to the five questions makes you uncomfortable, reconsider the approach. Responsible beats fast.